While 2SLS is the most widely used estimator for simuhaneous equation models, OLS may do better in finite samples. Here we demonstrate analytically that the for the widely used simultaneous equation model with one jointly endogenous variable and valid instruments, 2SLS has smaller MSE error, up to second order, than OLS unless the R , or the F statistic of the reduced form equation is extremely...

Two-stage least squares (2SLS) is commonly used to obtain consistent structural parameter inferences and asymptotically-valid inference results where one or more explanatory variables is – due to endogeneity, measurement error, etc.– correlated with the model error term. But the validity of 2SLS rests on an assumption that each of the instruments used is itself is unflawed – i.e., uncorrelated ...

The two-stage least-squares (2SLS) estimator is known to be biased when its first-stage fit is poor. I show that better first-stage prediction can alleviate this bias. In a two-stage linear regression model with Normal noise, I consider shrinkage in the estimation of the first-stage instrumental variable coefficients. For at least four instrumental variables and a single endogenous regressor, I...

Li, Fine, and Brookhart (2015) presented an extension of the two-stage least squares (2SLS) method for additive hazards models which requires an assumption that the censoring distribution is unrelated to the endogenous exposure variable. We present another extension of 2SLS that can address this limitation.

In an in uential paper in the June 1997 American Economic Review, Steven Levitt argues that there is an electoral cycle in police hiring, with faster hiring in election years and slower hiring in other years. He then uses elections as an instrument for police hiring to estimate the causal effect of police on crime. This comment points out that a weighting error in Levitt’s estimation procedure...

For a binary outcome Y, generated by simple threshold crossing model with single exogenous normally distributed explanatory variable X, the OLS estimator of coefficient on X in linear probability is consistent average partial effect X. Even this very setting, we show that when allowing for to be endogenously determined, 2SLS estimator, using instrumental Z, does not identify same causal paramet...

We perform an extensive series of Monte Carlo experiments to compare the performance of the “Jackknife Instrumental Variables Estimator,” or JIVE, with that of the more familiar 2SLS and LIML estimators. We find no evidence to suggest that JIVE should ever be used. It is always more dispersed than 2SLS, often very much so, and it is almost always inferior to LIML in all respects. Interestingly,...

This study used Monte Carlo simulations to examine the ability of the two-stage least squares (2SLS) estimator and two-stage residual inclusion (2SRI) estimators with varying forms of residuals to estimate the local average and population average treatment effect parameters in models with binary outcome, endogenous binary treatment, and single binary instrument. The rarity of the outcome and th...

This paper considers the IV estimation of spatial autoregressive models with endogenous regressors in the presence of many instruments. To improve asymptotic e¢ ciency, it may be desirable to use many valid instruments. However, …nite sample properties of IV estimators can be sensitive to the number of instruments. For a spatial model with endogenous regressors, this paper derives the asymptoti...